A polynomial-based circuit model combined with polynomial zonotope reachability analysis verifies analog neural networks under process variations, reducing verification time from days to seconds while enclosing 99% of variation samples across three datasets.
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New mutation operators and directed mutant generation produce more diverse faulty quantum neural network circuits than prior techniques, as shown in experiments.
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Formally Verifying Analog Neural Networks Under Process Variations Using Polynomial Zonotopes
A polynomial-based circuit model combined with polynomial zonotope reachability analysis verifies analog neural networks under process variations, reducing verification time from days to seconds while enclosing 99% of variation samples across three datasets.
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Efficient Mutation Testing of Quantum Machine Learning Models
New mutation operators and directed mutant generation produce more diverse faulty quantum neural network circuits than prior techniques, as shown in experiments.